Governance in work flow software

ABSTRACT

The disclosure presents categorization of users into groups comprising expert users and novice users. A system and method analyzes the users&#39; inputted data in helpdesk troubleshooting software to determine the deviation of novice users from expert users, or the deviation of novice users to a preconfigured behavior as determined by management policy. Other embodiments are also disclosed.

BACKGROUND

Tools which track users' activities and process and require a lot ofdiscretionary input are prone to deliberate or accidental misuse byusers. While many tools have input fields that, when filled out, canprovide useful information to management, they are usually ignored byusers and remain empty. For example, a tool from IBM® Rational®ClearQuest® provides a documentation field for the users' input, butdepending on the kind of defect fix, these fields may or may not be setas mandatory fields (IBM, Rational, and ClearQuest are registeredtrademarks of IBM Corporation in the United States, other countries, orboth). If not set as mandatory fields, they might not always be filledout by the users.

In addition, the tools may include estimation fields which are freeflowing text. New users or new corners into the organization may noteven be aware of the process and might never fill in these fields. Sincethe fields are optional, it is impossible to catch the violation untilit is discovered by brute force methods or discovered accidentally afterthe damage has been done. For example, it may be difficult to catch aviolation where a defect fix which required documentation never had anydocumentation because user never set the “documentation required” fieldto “mandatory.” Analyzing the difference between the behaviors of thedifferent users is also an issue with the current tools. Most tools arenot smart enough to realize similar mistakes made by similar users.

SUMMARY

In an embodiment, a method for troubleshooting archiving software toolsincludes categorizing a plurality of users into an expert group and anovice group, assigning the novice users to the novice group via a firstfuzzy logic membership function, and assigning expert users to theexpert group via a second fuzzy logic membership function.

One or more of the following features may be included. Inputs for aplurality of optional fields may be received from the users during useof the troubleshooting archiving software tool. Information about statusof the optional fields for the user may be stored.

The method may further include trouble shooting archiving software toolto indicate whether the optional fields are filled, partially filled orunfilled. First information may be analyzed to determine a firstcorrelation function between the first information and the novice usersmay be analyzed, and the first information may be analyzed to determinea second correlation function between the first information and theexpert users. A first statistical behavior of the novice users may becomputed over a first preconfigured interval using the first analysis,and second statistical behavior of the expert users may be computed overa second preconfigured interval using the second analysis. Differencesbetween the second statistical behavior to the first statisticalbehavior may be analyzed, and deviations within a first preconfiguredmargin may be detected using fuzzy logic. First deviation alerts mayalso be raised.

Alternatively, differences between the second statistical behavior to apredefined policy based behavior may be analyzed, deviations within asecond preconfigured margin may be detected using fuzzy logic andraising second deviation alerts.

When receiving the first or second deviation alerts, corrective actionsmay be triggered. The corrective actions may include: providing feedbackto the novice users; training the novice users; changing requirementsfor one or more of the optional fields from optional to mandatory; andadvising the novice users to consider filling one or more of theoptional fields when the novice users are providing the inputs to thetroubleshooting archiving software. Most of the first and secondanalysis may be performed during idle time of the troubleshootingarchiving software.

In another embodiment, a computer control logic for troubleshootingarchiving software tools, when executed on a process, is capable ofperforming categorizing a plurality of users into an expert group and anovice group, assigning the novice users to the novice group via a firstfuzzy logic membership function, and assigning expert users to theexpert group via a second fuzzy logic membership function.

One or more of the following features may be included. Inputs for aplurality of optional fields may be received from the users during useof the troubleshooting archiving software tool. Information about statusof the optional fields for the user may be stored.

The computer control logic may further be capable of performing troubleshooting archiving software tool to indicate whether the optional fieldsare filled, partially filled or unfilled. First information may beanalyzed to determine a first correlation function between the firstinformation and the novice users may be analyzed, and the firstinformation may be analyzed to determine a second correlation functionbetween the first information and the expert users. A first statisticalbehavior of the novice users may be computed over a first preconfiguredinterval using the first analysis, and second statistical behavior ofthe expert users may be computed over a second preconfigured intervalusing the second analysis. Differences between the second statisticalbehavior to the first statistical behavior may be analyzed, anddeviations within a first preconfigured margin may be detected usingfuzzy logic. First deviation alerts may also be raised.

Alternatively, differences between the second statistical behavior to apredefined policy based behavior may be analyzed, deviations within asecond preconfigured margin may be detected using fuzzy logic andraising second deviation alerts.

When receiving the first or second deviation alerts, corrective actionsmay be triggered. The corrective actions may include: providing feedbackto the novice users; training the novice users; changing requirementsfor one or more of the optional fields from optional to mandatory; andadvising the novice users to consider filling one or more of theoptional fields when the novice users are providing the inputs to thetroubleshooting archiving software. Most of the first and secondanalysis may be performed during idle time of the troubleshootingarchiving software.

In another embodiment, a data processing system comprising at least aprocessor and a memory for troubleshooting archiving software tools iscapable of performing categorizing a plurality of users into an expertgroup and a novice group, assigning the novice users to the novice groupvia a first fuzzy logic membership function, and assigning expert usersto the expert group via a second fuzzy logic membership function.

One or more of the following features may be included. Inputs for aplurality of optional fields may be received from the users during useof the troubleshooting archiving software tool. Information about statusof the optional fields for the user may be stored.

The computer control logic may further be capable of performing troubleshooting archiving software tool to indicate whether the optional fieldsare filled, partially filled or unfilled. First information may beanalyzed to determine a first correlation function between the firstinformation and the novice users may be analyzed, and the firstinformation may be analyzed to determine a second correlation functionbetween the first information and the expert users. A first statisticalbehavior of the novice users may be computed over a first preconfiguredinterval using the first analysis, and second statistical behavior ofthe expert users may be computed over a second preconfigured intervalusing the second analysis. Differences between the second statisticalbehavior to the first statistical behavior may be analyzed, anddeviations within a first preconfigured margin may be detected usingfuzzy logic. First deviation alerts may also be raised.

Alternatively, differences between the second statistical behavior to apredefined policy based behavior may be analyzed, deviations within asecond preconfigured margin may be detected using fuzzy logic andraising second deviation alerts.

When receiving the first or second deviation alerts, corrective actionsmay be triggered. The corrective actions may include: providing feedbackto the novice users; training the novice users; changing requirementsfor one or more of the optional fields from optional to mandatory; andadvising the novice users to consider filling one or more of theoptional fields when the novice users are providing the inputs to thetroubleshooting archiving software. Most of the first and secondanalysis may be performed during idle time of the troubleshootingarchiving software.

Embodiments of the invention related to a method, system and computerprogram product to categorize users into groups comprising of expert andnovice users may analyze the users' inputted data in a helpdesktroubleshooting archiving software tool to determine the deviation ofnovice users from expert users or the deviation of novice users to apreconfigured behavior as determined by management policy.

In a further embodiment, if the deviation is beyond a preconfiguredthreshold, a violation flag may be raised and the report is provided toupper management to take corrective action.

Yet a further embodiment of the invention may extract statisticalbehavior of each user as well as the statistical behavior of each usergroup, for example by using fuzzy logic.

In yet a further embodiment of the invention the method may enforcegovernance in the helpdesk troubleshooting archiving software tools.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not necessarily restrictive of the present disclosure. Theaccompanying drawings, which are incorporated in and constitute a partof the specification, illustrate subject matter of the disclosure.Together, the descriptions and the drawings serve to explain theprinciples of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

These and further aspects of exemplary embodiments the invention willbecome apparent from and will be elucidated hereinafter with respectwith reference made to the accompanying drawings. The drawingsillustrate only exemplary embodiments of the invention, and togetherwith the description, serve to further explain the embodimentsdisclosed. The numerous advantages of the disclosure may be betterunderstood by those skilled in the art by reference to the accompanyingfigures

FIG. 1 is an exemplary embodiment of an architecture 100 of theinvention;

FIG. 2 illustrates an exemplary embodiment of a method 200 to analyzeuser inputs; and

FIG. 3 is an exemplary embodiment of a data processing system 300 onwhich the method of FIG. 2 and the architecture of FIG. 1 may beimplemented.

DETAILED DESCRIPTION

Reference will now be made in detail to the subject matter disclosed,which is illustrated in the accompanying drawings.

Embodiments of the invention relate to categorizing users into expertusers and novice users. An advantage of the embodiments of the inventiondisclosed herein is to improve the quality of input of novice users intothe helpdesk troubleshooting archiving software tool. An example ofembodiment is a method that helps novice users in a troubleshootingarchiving software tool, for example a helpdesk troubleshootingarchiving software tool, which comprises of categorizing a plurality ofusers into expert group and novice group.

A further embodiment of the invention keeps novice users within theparameters of tool usage by expert users to ultimately enforce anygovernance. The division of users to two categories automaticallygenerates two or more user groups and provides the resources to analyzetheir input.

In yet a further embodiment analysis of the inputted data to determinethe correlation of novice to expert users and to extract the statisticalbehavior of each user as well as the statistical behavior of each usergroup using a fuzzy logic algorithm is done.

In yet a further embodiment of the invention is disclosed a method inwhich a tool can be made intelligent, by building intelligence into thetool, to determine problem areas in its usage. Most software configuresthe way they function according to the skill of users. However,embodiment of the invention teaches configuring guidance for one set ofusers based on the use by other set(s) of users.

In yet a further embodiment of the invention, the method determines thedeviation of novice users from expert users or the deviation of noviceusers to a preconfigured behavior as determined by management policy. Inone embodiment, if such deviation is beyond a preconfigured threshold, aviolation flag is raised and the report is provided to the managementfor corrective action.

Yet a further embodiment of the invention observes the behavior ofexpert users and compares such behavior against that of novice users andcomputers the deviations. For example, for optional fields it is likelythat most of the time they are left blank by novice users while it ismostly unlikely that the expert users leave such fields blank.

Exemplary embodiments of the invention are illustrated in FIG. 1 as anarchitecture 100 and FIG. 2 as a method 200, as described below.Depending on the expertise 202 of users, the user input 101, 201 isdivided into two categories: the expert user input 203 and the noviceuser input (204.) The embodiment enforces governance in the tools by notchanging the tools interface for different kinds of users, but learningfrom the users themselves.

Furthermore, in an exemplary embodiment, these user categories' input isstored, for example, in the System Database 103, 205. Each field of thehelpdesk software is categorized to be filled, unfilled, orpartially-filled, by each user.

In yet a further embodiment, an analysis engine 105, 206, for examplefuzzy logic based engine or neural network based engine or a Bayesianbelief based network etc., is plugged into the helpdesk troubleshootingarchiving software tool to do the analysis to determine the correlationto novice and expert users and to extract the statistical behavior ofeach user. It should be apparent to one skilled in the art that variousother kinds of analysis engines and different methods of comparing datacan be used in an embodiment of the invention.

Yet a further embodiment of the invention applies to workflow and/ortracking tools where for example a large number of fields are optionaland there is no objective or concrete way to determine or figure outwhether the input is correct.

In yet a further embodiment, if the deviation is beyond a preconfiguredthreshold, the violation flag 207 is set and as a result the programmanager 107 is notified 208. This way the program manager 107 can reviewthe novice users' input to determine if they are following or notfollowing the process correctly. Therefore, the problems are discoveredpro-actively by the tool, rather than much later in the cycle.

If the deviation is within the preconfigured threshold, no furtheraction (209) is taken. For example, an embodiment absorbs the policiesand processes of a parent site and internally compares the patterns ofusage at a new development site and pro-actively gives advice to theusers.

In one embodiment, these rules and policies are run in batch mode, forexample at idle times, with minimum user intervention.

Yet a further embodiment of the invention records previous history ofbehavior of all users and the status of the fields when they haveprovided input in their reports, which comprises of filled, partiallyfilled, or unfilled. Then, an analysis engine is used to derivecorrelation between the novice and expert users. The records of previoushistory of behavior can be classified and stored in a repository.

In a further embodiment, follows up with statistical analysis of thesaved information is done to determine the probability of each fieldbeing filled, partially filled or optionally filled for differentcategories of service calls. All such information is then analyzed usinga methodology such as a fuzzy logic, neural networks, Bayesian networksetc., to flag deviation between novice users and expert users and tosuggest corrective actions.

One embodiment of the invention assigns the novice users to the novicegroup and expert users to the expert group via for example a fuzzy logicmembership function. An embodiment of the invention receives differentinput for optional fields from different users during the use of thehelpdesk troubleshooting archiving software tool. The embodiment sortsinformation about status of optional fields for helpdesk troubleshootingarchiving software tool to indicate whether optional fields are filled,partially filled, or unfilled. This is done by analyzing the informationto determine a correlation function between information inputted bynovice users verses the expert users.

An example of the method is calculating statistical behavior of noviceor expert users over a preconfigured interval using analysis; andanalyzing differences between the statistical behaviors and detectingdeviations within a preconfigured margin using for example fuzzy logicetc., and raising deviation alerts which will result in triggeringcorrective actions when receiving the deviation alerts.

In one embodiment, the corrective actions comprises: providing feedbackto the novice users, training the novice users, changing requirementsfor one or more of the optional fields from optional to mandatory, andadvising the novice users to consider filling one or more of theoptional fields when the novice users are providing inputs to thehelpdesk troubleshooting archiving software. In an embodiment, most ofthe analysis is performed during idle time of helpdesk troubleshootingarchiving software.

A system, an apparatus, a device, or an article of manufacturecomprising one of the following items is an exemplary embodiment of theinvention: helpdesk troubleshooting archiving software tool, expertgroup, novice group, logic membership function, optional fields,correlation function, preconfigured interval, statistical behavior,deviations, preconfigured margin, deviation alerts, mandatory field, oridle time, applying the method mentioned above, for the purpose of thecurrent invention or governance in work flow software.

FIG. 3 illustrates a block diagram of an exemplary data processingsystem 300, for example a computing system such as a desktop computer,laptop computer, PDA, mobile phone and the likes, that can be used forimplementing exemplary embodiments of the present invention. Dataprocessing system 300 includes one or more processors, for exampleprocessor 304 as illustrated in FIG. 3. Processor 304 is coupled to acommunication infrastructure 302 (for example, a communications bus,cross-over bar, or network). Various software embodiments are describedin terms of this exemplary data processing system. After reading thisdescription, it will become apparent to a person of ordinary skill inthe relevant art(s) how to implement the invention using other dataprocessing systems and/or computer architectures.

Exemplary data processing system 300 can include display interface 308that forwards graphics, text, and other data from the communicationinfrastructure 302 (or from a frame buffer not shown) for display ondisplay unit 310. Data processing system 300 also includes main memory306, which can be random access memory (RAM), and may also includesecondary memory 312. Secondary memory 312 may include, for example,hard disk drive 314 and/or removable storage drive 316, representing afloppy disk drive, a magnetic tape drive, an optical disk drive, etc.Removable storage drive 316 reads from and/or writes to removablestorage unit 318 in a manner well known to those having ordinary skillin the art. Removable storage unit 318, represents, for example, afloppy disk, magnetic tape, optical disk, etc. which is read by andwritten to by removable storage drive 316. As will be appreciated,removable storage unit 318 includes a computer usable storage mediumhaving stored therein computer software and/or data.

In exemplary embodiments, secondary memory 312 may include other similarmeans for allowing computer programs or other instructions to be loadedinto the computer system. Such means may include, for example, removablestorage unit 322 and interface 320. Examples of such may include aprogram cartridge and cartridge interface, such as that found in videogame devices, a removable memory chip, such as an EPROM, or PROM andassociated socket, and other removable storage units 322 and interfaces320 which allow software and data to be transferred from removablestorage unit 322 to data processing system 300.

Data processing system 300 may also include a communications interface324. Communications interface 324 allows software and data to betransferred between the data processing system and any other externaldevices. Examples of communications interface 324 may include a modem, anetwork interface, such as an Ethernet card, a communications port, aPCMCIA slot and card, etc. Software and data transferred viacommunications interface 324 are typically in the form of signals whichmay be, for example, electronic, electromagnetic, optical, or othersignals capable of being received by communications interface 324. Thesesignals are provided to communications interface 324 via communicationspath (that is, channel) 326. Channel 326 carries signals and may beimplemented using wire or cable, fiber optics, a phone line, a cellularphone link, an RF link, and/or other communications channels.

The terms “computer program medium,” “computer usable medium,” and“computer readable medium” are used to generally refer to media such asmain memory 306 and secondary memory 312, removable storage drive 316, ahard disk installed in hard disk drive 314, and signals thereof.Computer program products are means for providing software to thecomputer system. The computer readable medium allows the computer systemto read data, instructions, messages or message packets, and othercomputer readable information from the computer readable medium. Thecomputer readable medium, for example, may include non-volatile memory,such as Floppy, ROM, Flash memory, Disk drive memory, CD-ROM, and otherpermanent storage. It can be used, for example, to transportinformation, such as data and computer instructions, between computersystems. Furthermore, the computer readable medium may comprise computerreadable information in a transitory state medium such as a network linkand/or a network interface, including a wired network or a wirelessnetwork, which allows a computer to read such computer readableinformation.

Computer programs, also called computer control logic, are typicallystored in main memory 306 and/or secondary memory 312. Computer programsmay also be received via communications interface 324. Such computerprograms, when executed, can enable the computer system to perform thefeatures of exemplary embodiments of the invention as discussed herein.In particular, computer programs, when executed, enable processor 304 toperform the features of data processing system 300. Accordingly, suchcomputer programs represent controllers of the data processing system.

Embodiments of the invention disclosed methods that may be implementedas sets of instructions or software readable by a device. Further, it isunderstood that the specific order or hierarchy of steps in the methodsdisclosed are examples of exemplary approaches. Based upon designpreferences, it is understood that the specific order or hierarchy ofsteps in the method can be rearranged while remaining within thedisclosed subject matter. The accompanying method claims presentelements of the various steps in a sample order, and are not necessarilymeant to be limited to the specific order or hierarchy presented.

The terms “certain embodiments”, “an embodiment”, “embodiment”,“embodiments”, “the embodiment”, “the embodiments”, “one or moreembodiments”, “some embodiments”, and “one embodiment” mean one or more(but not all) embodiments unless expressly specified otherwise. Theterms “including”, “comprising”, “having” and variations thereof mean“including but not limited to”, unless expressly specified otherwise.The enumerated listing of items does not imply that any or all of theitems are mutually exclusive, unless expressly specified otherwise. Theterms “a”, “an” and “the” mean “one or more”, unless expressly specifiedotherwise.

Further, although process steps, method steps or the like may bedescribed in a sequential order, such processes, methods and algorithmsmay be configured to work in alternate orders. In other words, anysequence or order of steps that may be described does not necessarilyindicate a requirement that the steps be performed in that order. Thesteps of processes described herein may be performed in any orderpractical. Further, some steps may be performed simultaneously, inparallel, or concurrently. Further, some or all steps may be performedin run-time mode.

When a single element or article is described herein, it will beapparent that more than one element/article (whether or not theycooperate) may be used in place of a single element/article. Similarly,where more than one element or article is described herein (whether ornot they cooperate), it will be apparent that a single element/articlemay be used in place of the more than one element or article. Thefunctionality and/or the features of an element may be alternativelyembodied by one or more other elements which are not explicitlydescribed as having such functionality/features. Thus, other embodimentsneed not include the element itself.

Although embodiments of the invention have been described with referenceto the embodiments described above, it will be evident that otherembodiments may be alternatively used to achieve the same object. Thescope is not limited to the embodiments described above, but can also beapplied to software programs and computer program products in general.It should be noted that the above-mentioned embodiments illustraterather than limit the invention and that those skilled in the art willbe able to design alternative embodiments without departing from thescope of the appended claims. In the claims, any reference signs shouldnot limit the scope of the claim. Embodiments of the invention can beimplemented by means of hardware comprising several distinct elements.

1. A method for troubleshooting archiving software tools, the methodcomprising: categorizing a plurality of users into an expert group and anovice group; assigning the novice users to the novice group via a firstfuzzy logic membership function; and assigning expert users to theexpert group via a second fuzzy logic membership function.
 2. The methodas claimed in claim 1, further comprising: receiving inputs for aplurality of optional fields from the plurality of users during use ofthe troubleshooting archiving software tool; and storing firstinformation about status of the optional fields for the user.
 3. Themethod as claimed in claims 1, further comprising: trouble shootingarchiving software tool to indicate whether the optional fields arefilled, partially filled or unfilled.
 4. The method as claimed in claim3, further comprising: first analyzing the first information todetermine a first correlation function between the first information andthe novice users; second analyzing the first information to determine asecond correlation function between the first information and the expertusers; computing first statistical behavior of the novice users over afirst preconfigured interval using the first analysis; and computingsecond statistical behavior of the expert users over a secondpreconfigured interval using the second analysis.
 5. The method asclaimed in claim 4, further comprising: analyzing differences betweenthe second statistical behavior to the first statistical behavior anddetecting deviations within a first preconfigured margin using fuzzylogic and raising first deviation alerts.
 6. The method as claimed inclaim 5, further comprising: alternatively analyzing differences betweenthe second statistical behavior to a predefined policy based behaviorand detecting deviations within a second preconfigured margin usingfuzzy logic and raising second deviation alerts.
 7. The method asclaimed in claim 1, further comprising: triggering corrective actionswhen receiving the first or second deviation alerts.
 8. The method asclaimed in claim 7, wherein the corrective actions comprises providingfeedback to the novice users; training the novice users; changingrequirements for one or more of the optional fields from optional tomandatory; and advising the novice users to consider filling one or moreof the optional fields when the novice users are providing the inputs tothe troubleshooting archiving software, and wherein most of the firstand second analysis is performed during idle time of the troubleshootingarchiving software
 9. A computer control logic for troubleshootingarchiving software tools, the logic when executed on a computer capableof performing: categorizing a plurality of users into an expert groupand a novice group; assigning the novice users to the novice group via afirst fuzzy logic membership function; and assigning expert users to theexpert group via a second fuzzy logic membership function.
 10. The logicas claimed in claim 9, further comprising: receiving inputs for aplurality of optional fields from the plurality of users during use ofthe troubleshooting archiving software tool; and storing firstinformation about status of the optional fields for the user.
 11. Thelogic as claimed in claims 9, further comprising: trouble shootingarchiving software tool to indicate whether the optional fields arefilled, partially filled or unfilled.
 12. The logic as claimed in claim11, further comprising: first analyzing the first information todetermine a first correlation function between the first information andthe novice users; second analyzing the first information to determine asecond correlation function between the first information and the expertusers; computing first statistical behavior of the novice users over afirst preconfigured interval using the first analysis; and computingsecond statistical behavior of the expert users over a secondpreconfigured interval using the second analysis.
 13. The logic asclaimed in claim 12, further comprising: analyzing differences betweenthe second statistical behavior to the first statistical behavior anddetecting deviations within a first preconfigured margin using fuzzylogic and raising first deviation alerts.
 14. The logic as claimed inclaim 13, further comprising: alternatively analyzing differencesbetween the second statistical behavior to a predefined policy basedbehavior and detecting deviations within a second preconfigured marginusing fuzzy logic and raising second deviation alerts.
 15. The logic asclaimed in claim 9, further comprising: triggering corrective actionswhen receiving the first or second deviation alerts.
 16. The logic asclaimed in claim 15, wherein the corrective actions comprise: providingfeedback to the novice users; training the novice users; changingrequirements for one or more of the optional fields from optional tomandatory; and advising the novice users to consider filling one or moreof the optional fields when the novice users are providing the inputs tothe troubleshooting archiving software, and wherein most of the firstand second analysis is performed during idle time of the troubleshootingarchiving software
 17. A data processing system comprising at least aprocessor and a memory for troubleshooting archiving software tools, thedata processing system capable of performing: categorizing a pluralityof users into an expert group and a novice group; assigning the noviceusers to the novice group via a first fuzzy logic membership function;and assigning expert users to the expert group via a second fuzzy logicmembership function.
 18. The data processing system as claimed in claim17, further comprising: receiving inputs for a plurality of optionalfields from the plurality of users during use of the troubleshootingarchiving software tool; storing first information about status of theoptional fields for the user; trouble shooting archiving software toolto indicate whether the optional fields are filled, partially filled orunfilled; first analyzing the first information to determine a firstcorrelation function between the first information and the novice users;second analyzing the first information to determine a second correlationfunction between the first information and the expert users; computingfirst statistical behavior of the novice users over a firstpreconfigured interval using the first analysis; and computing secondstatistical behavior of the expert users over a second preconfiguredinterval using the second analysis.
 19. The data processing system asclaimed in claim 18, further comprising: analyzing differences betweenthe second statistical behavior to the first statistical behavior anddetecting deviations within a first preconfigured margin using fuzzylogic and raising first deviation alerts; alternatively analyzingdifferences between the second statistical behavior to a predefinedpolicy based behavior and detecting deviations within a secondpreconfigured margin using fuzzy logic and raising second deviationalerts; and triggering corrective actions when receiving the first orsecond deviation alerts.
 20. The data processing system as claimed inclaim 19, wherein the corrective actions comprise: providing feedback tothe novice users; training the novice users; changing requirements forone or more of the optional fields from optional to mandatory; andadvising the novice users to consider filling one or more of theoptional fields when the novice users are providing the inputs to thetroubleshooting archiving software, and wherein most of the first andsecond analysis is performed during idle time of the troubleshootingarchiving software.